The expertise issue is aided by advances in flexible code packages in e.g. Python (PyTorch [1]}, scikit-learn [2]}, COMBO [3]}, pymatgen [4]}, Magpie [5]}, JARVIS [6]}), MATLAB (Statistics and Machine Learning Toolbox [7]}, Deep Learning Toolbox [8]}), and R (caret [9]}, e1071 [10]}, nnet [11]}) (see also Table 2 of [12]}), which shifts some of the burden of computational optimization, speed, and flexibility away from materials scientists and engineers. Additionally, experimental (e.g. arc melting [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]} and cms [20]}, [21]}) and computational (e.g. dft [22]}, [23]}, [24]}, [25]}, [26]}, [27]}, [28]}, [29]}, [30]}, [31]} and fem [32]}, [33]}) high throughput techniques and materials databases/tools such as the Materials Project [34]}, Open Quantum Materials Database [35]}, Pearson's Crystal Database [36]}, Matminer [37]}, Dark Reactions Project [38]}, 2D Perovskites Database, Energy Materials Datamining, and a battery materials database (see also Table 3 of [12]}) are available. These techniques, databases, and tools allow for consistent, curated datasets to be more easily produced, accessed, and added to. Thus, for experimental and computational scientists and engineers, an in-depth knowledge of ml algorithms or experimental/computational data production methods may not be necessary to leverage data-driven materials predictions. However, it is likely that when datasets are used for materials discovery, an understanding of the strengths and weaknesses of various algorithms, effect of parameters, and database entry details will improve prediction results. Some publications may also give recommendations of potential, promising compounds for the materials community which are then open for other groups to test [23]}.
